Autonomous Image Processing Algorithms Locate Region-of-Interests: The Mars Rover Application

In this report, we demonstrate that bottom-up IPA's, image-processing algorithms, can perform a new visual task to select and locate Regions-Of-Interests (ROIs). This task has been defined on the basis of a theory of top-down human vision, the scanpath theory. Further, using measures, Sp and Ss, the similarity of location and ordering, respectively, developed over the years in studying human perception and the active looking role of eye movements, we could quantify the efficient and efficacious manner that IPAs can imitate human vision in located ROIS. The means to quantitatively evaluate IPA performance has been an important part of our study. In fact, these measures were essential in choosing from the initial wide variety of IPAS, that particular one that best serves for a type of picture and for a required task. It should be emphasized that the selection of efficient IPAs has depended upon their correlation with actual human chosen ROIs for the same type of picture and for the same required task accomplishment

What Europe Knows and Thinks About Algorithms Results of a Representative Survey. Bertelsmann Stiftung eupinions February 2019

We live in an algorithmic world. Day by day, each of us is affected by decisions that algorithms make for and about us – generally without us being aware of or consciously perceiving this. Personalized advertisements in social media, the invitation to a job interview, the assessment of our creditworthiness – in all these cases, algorithms already play a significant role – and their importance is growing, day by day. The algorithmic revolution in our daily lives undoubtedly brings with it great opportunities. Algorithms are masters at handling complexity. They can manage huge amounts of data quickly and efficiently, processing it consistently every time. Where humans reach their cognitive limits, find themselves making decisions influenced by the day’s events or feelings, or let themselves be influenced by existing prejudices, algorithmic systems can be used to benefit society. For example, according to a study by the Expert Council of German Foundations on Integration and Migration, automotive mechatronic engineers with Turkish names must submit about 50 percent more applications than candidates with German names before being invited to an in-person job interview (Schneider, Yemane and Weinmann 2014). If an algorithm were to make this decision, such discrimination could be prevented. However, automated decisions also carry significant risks: Algorithms can reproduce existing societal discrimination and reinforce social inequality, for example, if computers, using historical data as a basis, identify the male gender as a labor-market success factor, and thus systematically discard job applications from woman, as recently took place at Amazon (Nickel 2018)

ASlib: A Benchmark Library for Algorithm Selection

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Projection-Based and Look Ahead Strategies for Atom Selection

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look ahead strategy requires a higher computational resource. To achieve a trade-off between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.Comment: sparsity, compressive sensing; IEEE Trans on Signal Processing 201

Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations